Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909539697164288 |
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| author | Liu, Yu Jiang, Hanbin Zhu, Lei Zhang, Yu Mao, Yuqi Cao, Jiangxia Pang, Shuchao |
| author_facet | Liu, Yu Jiang, Hanbin Zhu, Lei Zhang, Yu Mao, Yuqi Cao, Jiangxia Pang, Shuchao |
| contents | In the real world, users always have multiple interests while surfing different services to enrich their daily lives, e.g., watching hot short videos/live streamings. To describe user interests precisely for a better user experience, the recent literature proposes cross-domain techniques by transferring the other related services (a.k.a. domain) knowledge to enhance the accuracy of target service prediction. In practice, naive cross-domain techniques typically require there exist some overlapped users, and sharing overall information across domains, including user historical logs, user/item embeddings, and model parameter checkpoints. Nevertheless, other domain's user-side historical logs and embeddings are not always available in real-world RecSys designing, since users may be totally non-overlapped across domains, or the privacy-preserving policy limits the personalized information sharing across domains. Thereby, a challenging but valuable problem is raised: How to empower target domain prediction accuracy by utilizing the other domain model parameters checkpoints only? To answer the question, we propose the FMoE-CDSR, which explores the non-overlapped cross-domain sequential recommendation scenario from the federated learning perspective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation Liu, Yu Jiang, Hanbin Zhu, Lei Zhang, Yu Mao, Yuqi Cao, Jiangxia Pang, Shuchao Information Retrieval In the real world, users always have multiple interests while surfing different services to enrich their daily lives, e.g., watching hot short videos/live streamings. To describe user interests precisely for a better user experience, the recent literature proposes cross-domain techniques by transferring the other related services (a.k.a. domain) knowledge to enhance the accuracy of target service prediction. In practice, naive cross-domain techniques typically require there exist some overlapped users, and sharing overall information across domains, including user historical logs, user/item embeddings, and model parameter checkpoints. Nevertheless, other domain's user-side historical logs and embeddings are not always available in real-world RecSys designing, since users may be totally non-overlapped across domains, or the privacy-preserving policy limits the personalized information sharing across domains. Thereby, a challenging but valuable problem is raised: How to empower target domain prediction accuracy by utilizing the other domain model parameters checkpoints only? To answer the question, we propose the FMoE-CDSR, which explores the non-overlapped cross-domain sequential recommendation scenario from the federated learning perspective. |
| title | Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2503.13254 |